Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “data analysis”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9Linked to original sources
PubMed1986

[Conformation of the dienone system of delta 4,9-19-nor-3-ketosteroids from x-ray analysis data and its relation to reception and hormonal activity].

Basing on the data of the X-ray analysis of 2 beta-methylestr-4,9-dien-3-one-17 beta-ol (C19H26O2) and A-ring unsubstituted steroid 4,9-dien-3-ones, the noncomplanarity and flexibility of the conjugated dienone system in these transformed steroids has been demonstrated. The results have been also confirmed by UV spectroscopy. The ability of the dienone system to assume conformations with the out of- plane C3 = O3 and/or C9 = C10 bonds allows the AB-fragment of the steroid molecule to adopt the conformations required for interacting with various receptors. This property may also account for a simultaneous enhancement of several hormonal activities upon such a modification of steroids. The results of the X-ray analysis of 2 beta-methylestra-4,9-dien-3-one-17 beta-ol (space group P2(1)2(1)2(1), a 8,543(2), b 9,783(2), c 18,690(7) A, R 8,7%) are presented.

Ketosteroids↗
PubMed1988

Exponential data-analysis of passive-avoidance behavior in rats and mice.

Data obtained with the passive-avoidance task are usually presented as the median values of the latencies to respond. In an earlier publication we described a better way of presenting such data based on the observation that the complement of the cumulative distribution of step-through latencies can be closely fitted by a simple exponential function. Thus the "step-through rate constant" (STRC) is concise and accurate quantitative description of population behavior in this test. In this paper we present two examples of the application of this procedure. In the first, variation in the interval between training and testing in rats changes the STRCs of the different groups. In the second (based on data published by Flood et al.) administration of cycloheximide is seen to partition the experimental population of mice into two subgroups with different STRCs.

Animals↗
PubMed1979

A versatile physiological data analysis system using an Intel 8080 microprocessor.

A microprocessor based physiological data processor has been realised. The system controls the data flow from physiological experiments and performs on-line mean and variance calculations with an output in graphical form. The analyser accepts one data point every 0.5 ms and has a capacity of 128 records each containing 800 data points. Post stimulus histogram and interval histogram analysis programs have also been written and implemented.

Computers↗
PubMed1984

Design and analysis of accelerated degradation tests for the stability of biological standards II. A flexible computer program for data analysis.

The accelerated degradation test is commonly used to predict the stability of a biological standard during long-term storage at low temperature. A flexible computer program is described which has been written to analyse degradation test results by the method of maximum likelihood. In addition to predicting the degradation rate at low temperature, the program furnishes estimates of statistical precision and it carried out a test of goodness of fit of the data to the assumed Arrhenius equation model.

Biological Products↗
PubMed1980

Biological component of the NIMH clinical research branch collaborative program on the psychobiology of depression: II. Methodology and data analysis.

A preceding paper has reviewed the history, background, and rationale for this collaborative effort exploring the biologic basis of the affective disorders. This paper details the "flow" of a subject through the experimental protocol, the instrumentation used to obtain the clinical and behavioural data, and the biologic methodologies employed in the analysis of the body fluids. Data management and analysis techniques developed for this study are also examined.

Adrenocorticotropic Hormone↗
PubMed1990

Microcomputer-assisted multivariate survival data analysis using Cox's proportional hazards regression model.

We describe a microcomputer program (COXSURV) for proportional hazards multiple regression analysis of survival and other failure-time data generated in clinical trials and in retrospective clinical epidemiology studies. COXSURV is menu-driven and has powerful variable factoring and data exploratory capabilities for multivariate modeling. A batch mode allows automatic uni- or multivariate analyses for confounder summarization. Model selection for predictive purposes is possible through a step-up algorithm. The partial likelihood method used in the program allows the use of either discrete or continuous time scales by treating tied uncensored observations by either the exact method or by a robust approximation method. The program calculates most standard model fitting statistics for either overall or stratified analyses and uses data layout files compatible with those of other related epidemiologic analysis software.

Algorithms↗
PubMed2025

AmpSeqR: an R package for amplicon deep sequencing data analysis.

Amplicon sequencing (AmpSeq) is a methodology that targets specific genomic regions of interest for polymerase chain reaction (PCR) amplification so that they can be sequenced to a high depth of coverage. Amplicons are typically chosen to be highly polymorphic, usually with several highly informative, high frequency single nucleotide polymorphisms (SNPs) segregating in an amplicon of 100-200 base pair (bp). This allows high sensitivity detection and quantification of the frequency of each sequence within each sample making it suitable for applications such as low frequency somatic mosaicism detection or minor clone detection in mixed samples. AmpSeq is being increasingly applied to both biological and medical studies, in applications such as cancer, infectious diseases and brain mosaicism studies. Current bioinformatics pipelines for AmpSeq data processing lack downstream analysis, have difficulty distinguishing between true sequences and PCR sequencing errors and artifacts, and often require bioinformatic expertise. We present a new R package: AmpSeqR, designed for the processing of deep short-read amplicon sequencing data, with a focus on infectious diseases. The pipeline integrates several existing R packages combining them with newly developed functions to perform optimal filtering of reads to remove noise and improve the accuracy of the detected sequences data, permitting detection of very low frequency clones in mixed samples. The package provides useful functions including data pre-processing, amplicon sequence variants (ASVs) estimation, data post-processing, data visualization, and automatically generates a comprehensive Rmarkdown report that contains all essential results facilitating easy inclusion into reports and publications. AmpSeqR is publicly available at https://github.com/bahlolab/AmpSeqR.

High-Throughput Nucleotide Sequencing↗
PubMed1982

Computer-based monitoring and data analysis in anaesthesia and intensive care.

A computer with a software package for physiological monitoring at the bedside has been set up, modified and used in a Department of Anaesthesia and Intensive Care over the last three and a half years. Many difficulties have been experienced in implementing a useful computer-based program for monitoring physiological data. The cost of further development to overcome these difficulties could not be justified, and demands for computer time to allow storage and analysis fo other data was increasing. A decision was therefore made to eliminate the monitoring role of the computer, and it is now used for storage and analysis of administrative and clinical data from the Intensive Care Unit, Operating Theatres and Pain Management Unit.

Anesthesiology↗
PubMed2026

SimpleMicrobiome: An integrated web-based platform for streamlined microbiome data analysis and visualization.

Microbiome studies require multiple analytical steps after initial sequence processing. These steps commonly include data harmonization, preprocessing, taxonomic profiling, diversity analysis, differential abundance testing, predictive modeling, network inference, and preparation of publication-ready outputs. Although robust packages are available for many of these tasks, routine use often depends on command-line workflows, repeated data reformatting, and method-specific scripting. These requirements can limit accessibility for experimental researchers and complicate consistent analysis across interdisciplinary teams. We developed SimpleMicrobiome, a web-based R Shiny platform that integrates established microbiome analysis methods into a single interactive downstream workflow. The application accepts standard abundance, taxonomy, and metadata tables, supports interactive preprocessing and sample filtering, and provides modules for taxa profile visualization, alpha and beta diversity analysis, ANCOM-BC2 and MaAsLin2 differential abundance testing, Random Forest modeling with SHAP-based interpretation, microbial association network inference using SparCC and SPIEC-EASI through NetCoMi, correlation heatmaps, and dbRDA/CAP-style association biplots. The platform is implemented as a modular Shiny application so that preprocessing choices are propagated across downstream analyses, results can be exported as figures and tables, and the same application can be run through the public server, source-code installation, or a Docker image. SimpleMicrobiome consolidates major downstream microbiome analysis tasks in an accessible browser-based environment while retaining links to established analytical frameworks. The platform may reduce technical barriers for non-programming users, improve consistency across exploratory and reporting-oriented analyses, and support collaborative microbiome research. The public application is available at https://simplemicrobiome.mglab.org, the source code is available at https://github.com/yjcho2252/SimpleMicrobiome, and a Docker image for local deployment is available at https://hub.docker.com/r/mglab2252/simplemicrobiome.

differential abundance↗
PubMed1975

Multivariate data analysis based on a computerized patient monitoring system.

Multivariate time series data in post-operative patients (respiratory and cardiovascular) are compared to reference groups. Using this technique under the program control of a computerized patient monitoring system (IBM 1800) various classes in the respiratory and cardiovascular spectrum can define the co-ordinate system in hyperspace. The patient in crisis is recognised by his deviation from normal rates of change of the variable set, as well as by the time trajectories of recovery in the hyperspace.

Computers↗
PubMed1991

Experiences in data analysis and modelling with a multichannel biomagnetic system.

Evaluation of MEG/MCG data, measured with the Siemens biomagnetic multichannel system KRENIKON, in patients with epilepsy, infarction, Wolff-Parkinson-White (WPW) syndrome or extra systoles are in good agreement with the results of different investigation techniques. The evaluations have been performed using an equivalent current dipole model within a sphere or a half-space with homogeneous conductivity. In cases where the current dipole model is not adequate, multiple dipoles or complete distributions of current sources have to be considered. Results from simulations and applications to in vivo data and the influence of geometries better adjusted to realistic geometries are discussed.

Brain↗